To become carbon neutral by 2050, the European Union (EU27) net carbon sink from forests should increase from the current level of about −360 to −450 Mt CO_2eq yr⁻¹ by 2050. Reaching this target requires additional efforts, which should be informed by the expected interactions between current age-class distributions, the effect of forest management practices and the expected impacts of future climate change. However, modelling the combined effect of these drivers is challenging, since it requires a mechanistic assessment of climate impacts on primary productivity and heterotrophic respiration and a detailed representation of the forest age structure and of the management practices across the entire EU. To achieve this goal, we combined the output provided by four land–climate models – run under two different Representative Concentration Pathway scenarios (RCP2.6 and RCP6.0) – to parameterize the input data used in an empirical forest growth model. This hybrid modelling approach aims to quantify the impact of climate change and forest management on the long-term (i.e. to 2100) evolution of the EU27 + UK forest carbon budget. This approach was tested using a business-as-usual (BAU) scenario, based on the continuation of the management practices applied by EU member states within the historical period 2000–2015. We emphasize that our study does not explore a specific policy scenario but describes a methodological framework. Our results highlight that, under our BAU case, the EU27 + UK forest C sink would decrease to about −250 Mt CO_2eq yr⁻¹ in 2050 and −80 Mt CO_2eq yr⁻¹ by 2100. The main driver of the long-term evolution of the forest C sink is the ongoing ageing process of the European forests, mostly determined by past and ongoing management. In addition, climate change may further amplify or mitigate this trend. Due to the large uncertainty in climate projections, in 2050 the net C sink may range from −100 to −400 Mt CO_2eq yr⁻¹ under RCP2.6 and from −100 to −300 Mt CO_2eq yr⁻¹ under RCP6.0. These results suggest that while a change in management practices would be needed to reverse an otherwise declining trend in the sink, climate change adds a considerable uncertainty, potentially nearly doubling or halving the sink associated with management.

Emission of greenhouse gases (GHGs) and removals from land, including both anthropogenic and natural fluxes, require reliable quantification, including estimates of uncertainties, to support credible mitigation action under the Paris Agreement. This study provides a state-of-the-art scientific overview of bottom-up anthropogenic emissions data from agriculture, forestry and other land use (AFOLU) in the European Union (EU28 ¹ ). The data integrate recent AFOLU emission inventories with ecosystem data and land carbon models and summarize GHG emissions and removals over the period 1990–2016. This compilation of bottom-up estimates of the AFOLU GHG emissions of European national greenhouse gas inventories (NGHGIs), with those of land carbon models and observation-based estimates of large-scale GHG fluxes, aims at improving the overall estimates of the GHG balance in Europe with respect to land GHG emissions and removals. Whenever available, we present uncertainties, its propagation and role in the comparison of different estimates. While NGHGI data for the EU28 provide consistent quantification of uncertainty following the established IPCC Guidelines, uncertainty in the estimates produced with other methods needs to account for both within model uncertainty and the spread from different model results. The largest inconsistencies between EU28 estimates are mainly due to different sources of data related to human activity, referred to here as activity data (AD) and methodologies (tiers) used for calculating emissions and removals from AFOLU sectors. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.3662371 (Petrescu et al., 2020).

The new rules for the Land Use, Land Use Change and Forestry sector under the Kyoto Protocol recognized the importance of Harvested Wood Products (HWP) in climate change mitigation. We used the Tier 2 method proposed in the 2013 IPCC KP Supplement to estimate emissions and removals from HWP from 1990 to 2030 in EU-28 countries with three future harvest scenarios (constant historical average, and +/−20% in 2030). For the historical period (2000–2012) our results are consistent with other studies, indicating a HWP sink equal on average to −44.0 Mt CO2 yr−1 (about 10% of the sink by forest pools). Assuming a constant historical harvest scenario and future distribution of the total harvest among each commodity, the HWP sink decreases to −22.9 Mt CO2 yr−1 in 2030. The increasing and decreasing harvest scenarios produced a HWP sink of −43.2 and −9.0 Mt CO2 yr−1 in 2030, respectively. Other factors may play an important role on HWP sink, including: (i) the relative share of different wood products, and (ii) the combined effect of production, import and export on the domestic production of each commodity. Maintaining a constant historical harvest, the HWP sink will slowly tend to saturate, i.e. to approach zero in the long term. The current HWP sink will be maintained only by further increasing the current harvest; however, this will tend to reduce the current sink in forest biomass, at least in the short term. Overall, our results suggest that: (i) there is limited potential for additional HWP sink in the EU; (ii) the HWP mitigation potential should be analyzed in conjunction with other mitigation components (e.g. sink in forest biomass, energy and material substitution by wood).

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO₂), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO₂ for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well-characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results and inverse modeling estimates over the period 1990–2018. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGIs, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the member states following the IPCC Guidelines. Variation in estimates produced with other methods, like atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arises from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGIs with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO₂ fossil) and the use of different land use definitions for reporting emissions from land use, land use change and forestry (LULUCF) activities (CO₂ land). At the EU27 + UK level, the NGHGI (2019) fossil CO₂ emissions (including cement production) account for 2624 Tg CO₂ in 2014 while all the other seven bottom-up sources are consistent with the NGHGIs and report a mean of 2588 (± 463 Tg CO₂). The inversion reports 2700 Tg CO₂ (± 480 Tg CO₂), which is well in line with the national inventories. Over 2011–2015, the CO₂ land sources and sinks from NGHGI estimates report −90 Tg C yr⁻¹ ± 30 Tg C yr⁻¹ while all other BU approaches report a mean sink of −98 Tg C yr⁻¹ (± 362 Tg of C from dynamic global vegetation models only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr⁻¹ ± 400 Tg C yr⁻¹). This concludes that (a) current independent approaches are consistent with NGHGIs and (b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of “CO₂ flux” obtained from different approaches. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4626578 (Petrescu et al., 2020a).